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Course Outline

Overview of LLM Architecture and Attack Surface

  • How LLMs are built, deployed, and accessed via APIs.
  • Key components in LLM app stacks (e.g., prompts, agents, memory, APIs).
  • Where and how security issues arise in real-world use.

Prompt Injection and Jailbreak Attacks

  • What is prompt injection and why it’s dangerous.
  • Direct and indirect prompt injection scenarios.
  • Jailbreaking techniques to bypass safety filters.
  • Detection and mitigation strategies.

Data Leakage and Privacy Risks

  • Accidental data exposure through responses.
  • PII leaks and model memory misuse.
  • Designing privacy-conscious prompts and retrieval-augmented generation (RAG).

LLM Output Filtering and Guarding

  • Using Guardrails AI for content filtering and validation.
  • Defining output schemas and constraints.
  • Monitoring and logging unsafe outputs.

Human-in-the-Loop and Workflow Approaches

  • Where and when to introduce human oversight.
  • Approval queues, scoring thresholds, fallback handling.
  • Trust calibration and role of explainability.

Secure LLM App Design Patterns

  • Least privilege and sandboxing for API calls and agents.
  • Rate limiting, throttling, and abuse detection.
  • Robust chaining with LangChain and prompt isolation.

Compliance, Logging, and Governance

  • Ensuring auditability of LLM outputs.
  • Maintaining traceability and prompt/version control.
  • Aligning with internal security policies and regulatory needs.

Summary and Next Steps

Requirements

  • A solid understanding of large language models and prompt-based interfaces.
  • Experience in developing LLM applications using Python.
  • Familiarity with API integrations and cloud-based deployments.

Target Audience

  • AI developers.
  • Application and solution architects.
  • Technical product managers utilizing LLM tools.
 14 Hours

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